摘要
针对低分辨率下深度学习网络模型对图像特征识别能力有限、影响识别精度和速度等问题,提出一种卷积神经网络和共轭梯度法(convolutional neural networks-conjugate gradient,CNN-CG)模型。通过改进共轭梯度图特征识别算法,搭建卷积神经网络模型对图像进行特征识别和预测。将输入的图像由3维依次经过卷积层和激活函数投射到32,64,128和256维,编码器输出高维分类特征,经全连接层后得到图像类别信息。与其他模型在相同数据集上仿真实验比对,所提模型在识别精度、收敛情况、图像数据分析等方面表现优异,识别训练集准确率为100%,测试集准确率为80.26%,识别结果优于一般模型。结果表明:该模型在交通标志图像识别应用上有良好的效果,并表现出较强的鲁棒性和消融性,适合用作自动驾驶应用场景下交通标志图像识别模型的优化算法,且可以拓展到其他领域图像的特征识别。
In order to solve the problem that the deep learning network model has limited ability to recognize image features in low resolution,which affects the recognition accuracy and speed,a convolutional neural network-conjugate gradient(CNN-CG)model is proposed.By improving the conjugate gradient map feature recognition algorithm,the convolutional neural network model is built to recognize and predict the image features.The input image is projected from three dimensions to 32,64,128 and 256 dimensions through convolution layer and activation function in turn.The encoder outputs high-dimensional classification features,and the image category information is obtained after full connection layer.Compared with other models on the same data set,the proposed model performs well in terms of recognition accuracy,convergence and image data analysis.The recognition accuracy of the training set is 100%,and the accuracy of the test set is 80.26%,which is better than that of the general model.The results show that the model has a good effect on the traffic sign image recognition application,and shows strong robustness and ablation,which is suitable for the optimization algorithm of the traffic sign image recognition model in the automatic driving application scene,and can be extended to the feature recognition of images in other fields.
作者
文竹
于昊生
Wen Zhu;Yu Haosheng(School of Information Technology,Guangxi Police College,Nanning 530028,China;School of XuriBusiness Administration,Donghua University,Shanghai 200051,China)
出处
《兵工自动化》
北大核心
2025年第8期46-51,共6页
Ordnance Industry Automation
基金
广西哲学社会科学研究课题(24KSB008)。
关键词
自动驾驶
卷积神经网络
共轭梯度
特征识别
深度学习
autonomous driving
convolutional neural network
conjugate gradient
feature recognition
deep learning
